Dr Paul Yoo

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Overview
Overview
Biography
Prior to his current post at Birkbeck, he held academic/research posts in Cranfield (Defence Academy of the UK), Sydney (USyd) and South Korea (KAIST). He trained originally as a data scientist with degrees from the University of Sydney, Australia. In his career, he has amassed more than ninety prestigious journal and conference publications, has been awarded more than US$ 2.3 million in project funding, and a number of prestigious international and national awards for his work in advanced data analytics, machine learning and secure systems research, notably IEEE Outstanding Leadership Award, Rozetta Award (f.k.a CMCRC), Emirates Foundation Research Award, and the ICT Fund Award. Most recently, he won the prestigious Samsung award for research to protect IoT devices using machine-learning approach and Research England’s Global Challenge Research Fund (GCRF).
Paul currently serves as an Associate Editor for ACM Computing Surveys (Q1), IEEE Transactions on Sustainable Computing (Q1) and IEEE Access (Q1). He had served as an Editor for IEEE COMML (Q1) (big data and machine learning areas) from 2014 to 2019. He is also affiliated with the University of Sydney and Korea Advanced Institute of Science and Technology (KAIST) as a Visiting Professor. Paul is a Senior Member of the IEEE and a Fellow of HEA.
He is also a Founder and Chair of the BIDA's Threat Intelligence lab. This lab has set out on a journey to bridge the gap between the advancement of machine learning and the progression of cyber security with the objective of creating the next generation intelligent cyber defence and security research environment for future cyber warfare, including the applications of cyber physical systems. The lab is now a home for 11 PhD students.
His research focus lies in the theory and methodology of machine learning for large-scale real-world problems. He has successfully applied various machine learning and big data analytic approaches to a wide range of problem domains including security, biology, finance, industrial engineering, communications and education. His current interests include the application of machine learning and big data technologies in security and defence, finance and the engineering industry (e.g., autonomous vehicle) as well as a range of theoretical and methodological problems.
Paul has supervised a number of PhD/MSc by Research students to completion. Please feel free to contact him about research degree (PhD/MPhil) and internship opportunities.
Qualifications
- PhD in Engineering, University of Sydney, Australia, 2009
- Fellow, Higher Education Academy, 2019
Web profiles
Administrative responsibilities
- Programme Director, Digital Tech Solutions (Software)
- Deputy Director for Knowledge Exchange, Birkbeck Institute for Data Analytics (BIDA)
Professional memberships
Senior Member, IEEE
Honours and awards
- Samsung Global Research Outreach Award, Samsung, January 2017
- IEEE Outstanding Leadership Award, IEEE, January 2013
- Rozetta Award, Rozetta Institute, Australia, July 2006
ORCID
0000-0001-7665-8616 -
Supervision and teaching
Supervision and teaching
Supervision
Current doctoral researchers
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ZAID ALMAHMOUD
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ALBERTO MATUOZZO
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DILARA UYSAL
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GASSO MWALUSEKE
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GURMENDER ATWAL
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LAWRENCE OLUSANYA
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NEIL MACKINNON
Doctoral alumni
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SEONGIL HAN
Teaching
Teaching modules
- Foundations of Data Science II (BUCI070H5)
- Applied Machine Learning (BUCI077H7)
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Publications
Publications
Article
- Uysal, Dilara and Yoo, Paul and Kamal, T. (2022) Data-driven malware detection for 6G networks: a survey from the perspective of continuous learning and explainability via visualisation. IEEE Open Journal of Vehicular Technology ISSN 2644-1330.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. (2022) Detecting implicit cross-communities to which an active user belongs. PLoS One ISSN 1932-6203.
- Taha, K. and Yoo, Paul and Eddinari, F.Z. and Nedunkulathil, S. (2022) Inferring the densest multi-profiled cross-community for a user. Knowledge-Based Systems 237 (107681), ISSN 0950-7051.
- Al Hamadi, A. and Yeun, C.Y. and Damiani, E. and Yoo, Paul D. and Hu, J. and Yeun, H.K. and Yim, M.-S. (2021) Explainable Artificial Intelligence to evaluate industrial internal security using EEG signals in IoT Framework. Ad Hoc Networks 123 (102641), ISSN 1570-8705.
- Al Alkeem, E. and Yeun, C.Y. and Yun, J. and Yoo, Paul D. and Chae, M. and Rahman, A. and Asyhari, A.T. (2021) Robust deep identification using ECG and multimodal biometrics for Industrial Internet of Things. Ad Hoc Networks 121 (102581), ISSN 1570-8705.
- Taha, K. and Davuluri, R. and Yoo, Paul D. and Spencer, J. (2021) Personizing the prediction of future susceptibility to a specific disease. PLoS One ISSN 1932-6203.
- Al Hammadi, A. and Lee, D. and Yeun, C.Y. and Damiani, E. and Kim, S.-k. and Yoo, Paul D. and Choi, H.-j. (2020) Novel EEG sensor-based risk framework for the detection of insider threats in safety critical industrial infrastructure. IEEE Access 8, pp. 206222-206234. ISSN 2169-3536.
- Taha, K. and Yoo, Paul D. (2020) An effective disease risk indicator tool. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) ISSN 2694-0604.
- Lee, S.J. and Yoo, Paul D. and Asyhari, T.A. and Jhi, Y. and Chermak, L. and Yeun, C.Y. and Taha, K. (2020) IMPACT: Impersonation attack detection via edge computing using deep autoencoder and feature abstraction. IEEE Access 8, pp. 65520-65529. ISSN 2169-3536.
- Kim, S.-k. and Yeun, C.Y. and Yoo, Paul D. (2019) An enhanced machine learning-based biometric authentication system using RR- Interval Framed Electrocardiograms. IEEE Access 7, pp. 168669 -168674. ISSN 2169-3536.
- Li, M. and Selim, B. and Muhaidat, S. and Sofotasios, P. and Dianati, M. and Yoo, Paul D. and Liang, J. and Wang, A. (2019) Effects of residual hardware impairments on secure NOMA-based cooperative systems. IEEE Access 4, ISSN 2169-3536.
- Al Alkeem, E. and Kim, S.-K. and Yeun, C.Y. and Zemerly, J. and Poon, K. and Yoo, Paul D. (2019) An Enhanced Electrocardiogram biometric authentication system using machine learning. IEEE Access 7, pp. 123069-123075. ISSN 2169-3536.
- Yoo, Paul D. (2019) Popularity-based video caching techniques for cache-enabled networks: a survey. IEEE Access 7, pp. 27699-27719. ISSN 2169-3536.
- Yoo, Paul D. (2019) Censor-based cooperative Multi-Antenna Spectrum Sensing with Imperfect Reporting Channels. IEEE Transactions on Sustainable Computing 5 (1), pp. 48-60. ISSN 2377-3782.
- Yoo, Paul D. (2019) Shortlisting the influential members of criminal organizations and identifying their important communication channels. IEEE Transactions on Information Forensics and Security ISSN 1556-6013.
Book Section
- Kim, S.-K. and Yeun, C.Y. and Yoo, Paul and Lo, N.-W. and Damiani, E. (2022) Deep learning-based arrhythmia detection using RR-Interval framed electrocardiograms. In: The 8th International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems. Springer. (In Press)
- Parker, L. and Yoo, Paul D. and Asyhari, T. and Chermak, L. and Jhi, Y. and Taha, K. (2019) DEMISe: interpretable deep extraction and mutual information selection techniques for IoT intrusion detection. In: ARES '19 Proceedings of the 14th International Conference on Availability, Reliability and Security. ACM. ISBN 9781450371643.
- Yoo, Paul and Zhou, B.B. and Zomaya, A.Y. (2007) Machine intelligence in protein sequence analysis and structure prediction. In: Arabnia, H.R. and Yang, M.Q. and Yang, J.Y. (eds.) BIOCOMP 2007: International Conference on Bioinformatics & Computational Biology. CSREA Press. pp. 370-377.
- Yoo, Paul and Kim, M.H. and Jan, T. (2005) Machine learning techniques and use of event information for stock market prediction: a survey and evaluation. In: CIMCA 2005: International Conference on Computational Intelligence for Modelling Control and Automation. IEEE Computer Society. pp. 835-841. ISBN 9780769525040.
Editorial
- Yoo, Paul and Tari, Z. (2021) Sustainable information security and forensic computing. IEEE Transactions on Sustainable Computing 6 (1), pp. 2-3. IEEE Computer Society. ISSN 2377-3782.